Using the continuity of the entire roster would mask whether their player churn was from peripheral parts or the main engines themselves. 2019-2020 NBA Advanced Team Stats. Copyright © NBA Media Ventures, LLC. Below, I’ve plotted the weaker performing group of statistics, with lineup continuity running along the x-axis.
the official stats partner of the NBA. Data Provided By Watch our How-To Videos to Become a Stathead, Subscribe to Stathead and get access to more data than you can imagine. (!). “Value” describes the impact a player has in a given circumstance (e.g. And maybe that’s the ultimate takeaway here: The best one-number metrics — BPM, PIPM, RAPM, and (probably) RPM — are actually quite good at ball-parking player value, but until we can generate more context-specific numbers, we’re still going to have to do the legwork of analyzing circumstances manually to filter out some of the noise. Bryant, who is 19 and bypassed college basketball to play in the NBA, scored a team-high 33 points off the bench, and Jordan scored a team-high 36 points. What’s noteworthy here is the ratio between new team and “same” team variability. Question, Comment, Feedback, or Correction? We have tools and resources that can help you use sports data. The first composite stat to really take hold was John Hollinger’s Player Efficiency Rating (PER), which has been followed by further advancements on the box score along with metrics that harness plus-minus data, like ESPN’s Real Plus-Minus (RPM). GP Games Played W Wins L Losses MIN Minutes Played OffRtg Offensive Rating DefRtg Core players drive scheme and strategy and are often fulcrums of interactive effects in basketball. Career Opportunities. Copyright © 2000-2020 Sports Reference LLC. a big man on a team without any backups is more valuable than if he joined the Tim Duncan–David Robinson Spurs). I divided teams into different groups based on how much of their “core” lineup returned from the previous season. Notice how much better this group does handling new player combinations: Another major takeaway here is how relatively close all these stats are, especially compared to some of the Old Guard metrics from the previous group. Your privacy is safe with us. Jacob Goldstein’s PIPM uses a luck adjustment to minimize some of the noise in that stat’s plus-minus data, and it regularly outperforms AuPM. RPM looks about as good as these other top metrics in these tests, but certainly not better based on a few seasons of information. We'll never pass along your email address to spammers, scammers, or the like. To figure out what’s going on here, I extended an old Neil Paine study (who himself expanded on similar studies) by using prior season values to predict teams in the following year. Yet it still outperforms PER here! — but there’s something revealing here. (The error is actually RMSE, for the technically inclined.)
PPG: Points per game, the original player ranker, iSRS: individual SRS, a dummy metric that multiplies a player’s minutes by his team’s SRS (adjusted point differential). Furthermore, while box stats are helpful and relatively stable, combining them with plus-minus doesn’t necessarily bring us closer to a Holy Grail player metric.
iSRS arguably outperforms points per game here, so it might be wiser to cite minutes-on-a-good-team instead of raw points as an indicator of player quality. All rights reserved. For these tests, I also converted all metric-values to the same margin of victory (SRS) scale via a regression. AFP PHOTO VINCENT LAFORET (Photo credit should read VINCENT LAFORET/AFP/Getty Images). Scores from any date in BAA/NBA or ABA history.
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